Shivya Technologies
AI Deployment & Enablement
We design and deploy AI systems inside your existing infrastructure — not a bolt-on demo. Architecture decisions made with your data and compliance constraints in mind, infra built to scale from day one, and a team that stays through the scaling phase, not just the pilot.
What's included
Six things you're actually buying.
Architecture design
Not just model selection. We design data flow, integration points, and security boundaries around how your systems already work — so the AI layer fits your infrastructure instead of forcing a rebuild around it.
Infra support
Deployment environments, scaling paths, and cost/performance tuning, decided up front rather than discovered in production. This is where most AI pilots quietly die — we plan for it from day one.
Multi-cloud deployment
We deploy and operate across AWS, Azure, and GCP. Your existing cloud commitments and compliance constraints decide the architecture — we're not steering you toward one vendor's ecosystem.
Full-cycle build
From discovery through production deployment and ongoing scaling. One team across the whole lifecycle, not a handoff from a strategy firm to an engineering team that never spoke to each other.
Explainable, auditable AI
We work with how a model represents meaning internally — its vector/latent space — not just its output. That's what makes a wrong decision debuggable and a right one explainable to a regulator, not just plausible.
Cross-industry depth
The team has hands-on experience in insurance, banking/financial crime, fintech, healthcare, and regulatory/compliance-heavy domains — which is why "any industry" is a credible claim for us, not a slogan. See where we've worked.
Broken into stages
The same five-stage process, every time.
Consistency here is deliberate — it's also how the platform in R&D gets built: each stage surfaces reusable components. See the full walkthrough on the Approach page.
Architecture Assessment
Design
Deployment
Scaling Support
Optimization
Use cases
What this has actually looked like.
Anonymized — no client or company names — but real solution patterns, not hypothetical ones. Each card links through to the problem, the architecture, the stack, and the pipeline behind it.
FinTech / Payments
Real-Time Fraud & AML Detection
A rules-only fraud engine was declining too many good transactions while missing fraud rings that rules can't see, and AML alert queues were almost all noise.
A three-tier decision system: deterministic guardrails, a real-time ML risk model, and an LLM layer that explains and drafts — but never decides.
Digital Lending / NBFC
Agentic Underwriting & Document Processing
Manual underwriting on document-heavy loan files meant days of turnaround, inconsistent decisions, and underwriters spending most of their time transcribing instead of judging.
A document-to-decision pipeline: OCR plus LLM extraction with citations, specialist verification agents, and a deterministic policy engine that makes the actual credit decision.
Retail / D2C / Ecommerce
Conversational Commerce & Personalization
Keyword-only search missed real shopper intent, recommendations were computed overnight, and pre-purchase questions went unanswered — costing conversions and driving returns.
Hybrid semantic search, a grounded shopping assistant that never invents a price or stock status, and personalization that updates within the session, not the next day.
Healthcare / Life Sciences
Clinical & Regulatory Evidence Copilot
Producing regulatory documents was slow, source data was scattered across many systems, and a single wrong number is a data-integrity event, not just a typo.
A grounded generation system where every number traces to a deterministic calculation and a source citation, verified automatically before a human ever sees the draft.
Also increasingly part of an engagement
Two platform capabilities are starting to ship alongside deployments.
A gate that verifies an agent's output before it becomes an action, and a world model that lets you simulate a plan before you commit to it. Both are early, both come from the same deployment work described above, and both are covered in full on the Platform page.
Not sure which stage you're at?
Most conversations start with a 30-minute architecture review — no deck, just your current setup and where it's breaking.
Book a review